Visuals_Draft_04_14

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library(educationdata)
# Test Run with using get_education_data 
# data <- get_education_data(level = "college-university",
#     source = "ipeds",
#     topic = "directory",
#     filters = list(year = 2019))
# data

# Scorecard data - 2019 
sc <- read_csv('2019_College_Scorecard_Valid_Admissions_Data.csv')
sc

## change projection of sc data
sc <- sc %>%
  dplyr::mutate(uni_rank = case_when(
    ADM_RATE < 0.05 ~ 'elite',
    ADM_RATE < 0.2 ~ 'highly selective',
    ADM_RATE < 0.3 ~ 'more selective',
    ADM_RATE < 0.5 ~ 'selective',
    ADM_RATE < 0.7 ~ 'less selective',
    TRUE ~ 'not selective')) %>% mutate(uni_rank = factor(uni_rank, levels=c('not selective', 'less selective', 'selective', 'more selective', 'highly selective', 'elite')))

Simple Scattergram

First we are going to try to present this pattern for different tiers of universities (admission rate as well as debt)

# Remove PrivacySuppressed Records and transform Debt Median into a numeric value - we can also do this on the main sc df
sc$DEBT_MDN[is.na(sc$DEBT_MDN)] <- 0;

m <- sc %>% subset(DEBT_MDN !='PrivacySuppressed') %>% transform(DEBT_MDN = as.numeric(DEBT_MDN)) %>% 
              ggplot(., aes(x=ADM_RATE, y=DEBT_MDN,color=uni_rank)) +
  geom_point() +
  geom_smooth(color='#EA4F88', se = FALSE) +
  theme_ipsum(base_size = 10, axis_title_size = 12, plot_title_size=14) +
  scale_color_viridis_d()+
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank()) +
  scale_y_discrete(limits=c(0,10000,20000,30000), labels=c('0','10','20','30')) +
  labs(x='Admissions Rate', y='Median Loan Amount per Student\n(thousands)', 
       title='Student Debt and Admissions Rate',
       color='Selectivity')

Continuous limits supplied to discrete scale.
Did you mean `limits = factor(...)` or `scale_*_continuous()`?

m

Scattergram as Violin Plot

ipeds15 <- get_education_data(level = "college-university",
    source = "ipeds",
    topic = "grad-rates-pell",
    filters = list(year = 2015))
ipeds15

Student Debt over Time

#Process the data 
sc_time <- read_csv('2010_2019_student_debt.csv') 

Missing column names filled in: 'X1' [1]
── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  .default = col_double(),
  INSTNM = col_character(),
  CITY = col_character(),
  STABBR = col_character(),
  ZIP = col_character(),
  UNEMP_RATE = col_logical(),
  DEBT_MDN = col_character(),
  MN_EARN_WNE_P10 = col_logical(),
  MD_EARN_WNE_P10 = col_logical(),
  School = col_logical(),
  State = col_logical(),
  True = col_character(),
  ADM_RATE_ALL_1 = col_character()
)
ℹ Use `spec()` for the full column specifications.

28660 parsing failures.
 row    col           expected                  actual                         file
3223 UNITID a double           Community College       '2010_2019_student_debt.csv'
3223 School 1/0/T/F/TRUE/FALSE Kenai Peninsula College '2010_2019_student_debt.csv'
3223 State  1/0/T/F/TRUE/FALSE Alaska                  '2010_2019_student_debt.csv'
3224 UNITID a double           Community College       '2010_2019_student_debt.csv'
3224 School 1/0/T/F/TRUE/FALSE Kodiak College          '2010_2019_student_debt.csv'
.... ...... .................. ....................... ............................
See problems(...) for more details.

sc_time<- sc_time %>% subset(DEBT_MDN !='PrivacySuppressed') %>% 
  transform(DEBT_MDN = as.numeric(DEBT_MDN)) %>% 
  dplyr::mutate(DEBT_MDN = ifelse(is.na(DEBT_MDN), 0, DEBT_MDN))

# Additions of States df from Tigris File 
library(tigris)

To enable 
caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.

states <- states(cb = TRUE)

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sc_time

sc_time_year <- sc_time %>% group_by()

# States 
states_10 <- states %>% 
  inner_join(sc_time, by=c(STUSPS='STABBR')) 


popup1 <- paste("State:",states$NAME,"<br/>",
                 "Population:",states$population,"<br/>",
                 "Successful Projects / Population:",states$scaled_success,"<br/>")

#used 'success' measures. 
m <- leaflet(states) %>% addProviderTiles("CartoDB.Positron") %>%
  addPolygons(fillColor = ~pal(states$scaled_success),
              color = "white",
              weight = 0.5,
              fillOpacity = 0.7,  
              highlight = highlightOptions(
                weight = 5,
                color = "#666",
                fillOpacity = 0.7,
                bringToFront = TRUE),
                popup=popup1) %>%
  addLegend(position = "bottomleft",pal = pal, values = c(min(states$scaled_success),max(states$scaled_success)),title = "Successful Kickstarter Projects\n (scaled by state)") %>% 
  setView(-98.5795, 39.8282, zoom=3)

Error in setView(., -98.5795, 39.8282, zoom = 3) : 
  could not find function "setView"